la soutenance de thèse de Hassan Khotanlou aura lieu à l’ENST, salle B312 le jeudi 07 Février 2008 à 14H00.
Cette thèse porte sur :
"Segmentation 3D de tumeurs et de structures internes du cerveau en IRM"
Le jury est composé de :
- Mme. Su Ruan, Professeur, IUT/SIC, Troyes (Rapporteur)
- M. Christophe Léger, Maître de Conférences (HDR), Polytech’Orléans/LESI,
Orléans (Rapporteur)
- Mme. Nicole Vincent, Professeur, Université Paris 5/CRIP5, Paris(Examinateur)
- M. Hubert Cardot, Professeur, Polytech Tours/Dept.Informatique, Tours
(Examinateur)
- Mme. Isabelle Bloch, Professeur, ENST, Paris (Directeur de thèse).
Résumé des travaux :
Abstract
The main topic of this thesis is to segment brain tumors, their components
(edema and necrosis) and internal structures of the brain in 3D MR
images. For tumor segmentation we propose a framework that is a
combination of region-based and boundary-based paradigms. In this
framework, we first segment the brain using a method adapted for
pathological cases and extract some global information on the tumor by
symmetry-based histogram analysis. The second step segments the tumor and
its components. For this, we propose a new and original method that
combines region and boundary information in two phases : initialization and
refinement. For initialization, which is mostly region-based, we present
two new methods. The first one is a new fuzzy classification method which
combines the membership, typicality and neighborhood information of the
voxels. The second one relies on symmetry-based histogram analysis. The
initial segmentation of the tumor is refined relying on boundary
information of the image. This method is a deformable model constrained by
spatial relations. The spatial relations are obtained based on the initial
segmentation and surrounded tissues of the tumor. The proposed method can
be used for a large class of tumors in any modality of MR images. To
segment a tumor and its components full automatically the proposed
framework needs only a contrast enhanced T1-weighted image and a FLAIR
image. In the case of a contrast enhanced T1-weighted image only, some
user interaction will be needed.
We evaluated this method on a data set of 20 contrast enhanced
T1-weighted and 10 FLAIR images with different types of tumors.
Another aim of this thesis is the segmentation of internal brain structures in the presence of a tumor. For this, a priori knowledge about the anatomy and the spatial organization of the structures is provided by an ontology. To segment each structure, we first exploit its relative spatial position from a priori knowledge. We then select the spatial relations which remain consistent using the information on the segmented tumor. These spatial relations are then fuzzified and fused in a framework proposed by our group. As for the tumor, the segmentation process of each structure has two steps. In the first step we search the initial segmentation of the structure in a globally segmented brain. The search process is done in the region of interest (ROI) provided by the fused spatial relations. To globally segment the brain structures we use two methods, the first one is the proposed fuzzy classification and the second one is a multiphase level sets. To refine the initial segmentation, we use a deformable model which is again constrained by the fused spatial relations of the structure. This method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles, caudate nucleus and thalamus.
La soutenance sera suivie d’un pot auquel vous êtes tous chaleureusement conviés.